Investigating contaminant-related health effects in killer whales in British Columbia using omics
Bibliographic record
Abstract
Killer whales (Orcinus orca) are an iconic species in the Salish Sea with three populations inhabiting the area: the northern resident, southern resident, and Bigg’s populations. Low food availability, contaminant exposure, and noise are the major threats to these populations with the southern residents being the most vulnerable. We measured PCB and PBDE concentrations in blubber biopsies collected from individuals in the southern resident, northern resident, and Bigg’s populations between 2019 and 2021. Our data show differences in PCB and PBDE concentrations between populations and sex. Building upon this research, we are combining multiple omics approaches to deepen our understanding of contaminant-related health effects in these populations: 1) metabolomics using a targeted suite of 254 metabolites that include the following classes – energy metabolism, amino acids, biogenic amines, acylcarnitines, phosphatidylchlorlines, sphingomyelins, bile acids, hexose, and fatty acids, 2) transcriptomics with RNA-sequencing that will also allow us to identify key genes responsive to contaminant exposure. Building upon decades of research by our team, these findings will provide a clearer understanding of health effects associated with priority contaminants in killer whales that can be used to inform risk-based prioritization of conservation efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".